Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 2 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Algorithms | 2 |
| High School Students | 2 |
| Predictor Variables | 2 |
| Adolescents | 1 |
| Artificial Intelligence | 1 |
| Comparative Analysis | 1 |
| Correlation | 1 |
| Data Analysis | 1 |
| Dropout Characteristics | 1 |
| Dropouts | 1 |
| Effect Size | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 2 |
| Journal Articles | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| High Schools | 2 |
| Secondary Education | 2 |
Audience
| Policymakers | 1 |
| Researchers | 1 |
Location
Laws, Policies, & Programs
Assessments and Surveys
| National Longitudinal Study… | 1 |
What Works Clearinghouse Rating
Chenguang Pan; Zhou Zhang – International Educational Data Mining Society, 2024
There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…
Descriptors: High School Students, Dropouts, Dropout Characteristics, Artificial Intelligence
Duxbury, Scott W. – Sociological Methods & Research, 2023
This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model--even those uncorrelated with other predictors--or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot…
Descriptors: Graphs, Scaling, Research Problems, Models

Peer reviewed
Direct link
